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Abstract #0017

BrainParc: Unified Lifespan Brain Parcellation with Anatomy-guided Progressive Transmission

Jiameng Liu1, Feihong Liu1,2, Kaicong Sun1, Caiwen Jiang1, Yulin Wang1, Tianyang Sun3, Feng Shi3, and Dinggang Shen1,3,4
1School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China, 2School of Information Science and Technology, Northwest University, Xian, China, 3Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China, 4Shanghai Clinical Research and Trial Center, Shanghai, China

Synopsis

Keywords: Analysis/Processing, Brain, brain tissue segmentation, brain region parcellation

Motivation: Accurate brain parcellation across lifespan is essential for understanding brain development, aging, and disorder. Existing parcellation algorithms are typically tailored to specific ages or data centers to address intensity variations from brain maturation and scanner difference, leading to inconsistent parcellation across lifespan.

Goal(s): We propose a unified framework, BrainParc, for precise and consistent sMRI-based brain parcellation across lifespan.

Approach: BrainParc first hierarchically extracts coherent, intensity-invariant anatomical (edge map) from sMRI, and then utilizes the edge map to progressively guide parcellation of brain tissues and regions.

Results: Extensive experiments conducted on internal and external datasets consistently demonstrate superior performance and wide application of BrainParc.

Impact: We present BrainParc, the first lifespan brain parcellation framework using a single model, and evaluate it on the largest known lifespan sMRI dataset to date (over 91.3 thousand scans), achieving highest precision and consistency than other segmentation models.

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Keywords